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Merge branch 'feature/otwcp3' into develop

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This commit is contained in:
Sander Roosendaal
2017-06-19 07:44:08 +02:00
parent f16e5ddeb1 182dadf679
commit 96aeb8d5a4
5 changed files with 98 additions and 39 deletions
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1
rowers/dataprep.py
View File

@@ -430,6 +430,7 @@ def save_workout_database(f2,r,dosmooth=True,workouttype='rower',
delta,cpvalues,avgpower = datautils.getsinglecp(row.df)
if utils.isbreakthrough(delta,cpvalues,r.p0,r.p1,r.p2,r.p3):
isbreakthrough = True
res = datautils.updatecp(delta,cpvalues,r)
dtavg = row.df['TimeStamp (sec)'].diff().mean()

92
rowers/datautils.py
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@@ -1,6 +1,88 @@
import pandas as pd
import numpy as np
from scipy.interpolate import griddata
from scipy import optimize
def updatecp(delta,cpvalues,r):
cp2 = r.p0/(1+delta/r.p2)
cp2 += r.p1/(1+delta/r.p3)
delta = delta.append(delta)
cp = cpvalues.append(cp2)
powerdf = pd.DataFrame({
'Delta':delta,
'CP':cp,
})
powerdf.dropna(axis=0,inplace=True)
powerdf.sort_values(['Delta','CP'],ascending=[1,0],inplace=True)
powerdf.drop_duplicates(subset='Delta',keep='first',inplace=True)
res = cpfit(powerdf)
p1 = res[0]
r.p0 = p1[0]
r.p1 = p1[1]
r.p2 = p1[2]
r.p3 = p1[3]
r.save()
return 1
def cpfit(powerdf):
# Fit the data to thee parameter CP model
fitfunc = lambda pars,x: abs(pars[0])/(1+(x/abs(pars[2]))) + abs(pars[1])/(1+(x/abs(pars[3])))
errfunc = lambda pars,x,y: fitfunc(pars,x)-y
p0 = [500,350,10,8000]
p1 = p0
thesecs = powerdf['Delta']
theavpower = powerdf['CP']
if len(thesecs)>=4:
p1, success = optimize.leastsq(errfunc, p0[:], args = (thesecs,theavpower))
else:
factor = fitfunc(p0,thesecs.mean())/theavpower.mean()
p1 = [p0[0]/factor,p0[1]/factor,p0[2],p0[3]]
p1 = [abs(p) for p in p1]
fitt = pd.Series(10**(4*np.arange(100)/100.))
fitpower = fitfunc(p1,fitt)
fitpoints = fitfunc(p1,thesecs)
fitpoints0 = fitpoints.copy()
dd = fitpoints-theavpower
ddmin = dd.min()
frac = abs(ddmin)/fitpoints.mean()
while frac>0.0001:
fitpoints = fitpoints*(fitpoints.mean()-ddmin)/(fitpoints.mean())
dd = fitpoints-theavpower
ddmin = dd.min()
frac = abs(ddmin)/fitpoints.mean()
ratio = fitpoints.mean()/fitpoints0.mean()
return p1,fitt,fitpower,ratio
def getlogarr(maxt):
maxlog10 = np.log10(maxt-5)
logarr = np.log10(5.)+np.arange(50)*maxlog10/50.
logarr = [int(10.**(la)) for la in logarr]
logarr = pd.Series(logarr)
logarr.drop_duplicates(keep='first',inplace=True)
logarr = logarr.values
return logarr
def getsinglecp(df):
thesecs = df['TimeStamp (sec)'].max()-df['TimeStamp (sec)'].min()
@@ -9,13 +91,7 @@ def getsinglecp(df):
else:
maxt = 1000.
maxlog10 = np.log10(maxt)
logarr = np.arange(50)*maxlog10/50.
logarr = [int(10.**(la)) for la in logarr]
logarr = pd.Series(logarr)
logarr.drop_duplicates(keep='first',inplace=True)
logarr = logarr.values
logarr = getlogarr(maxt)
dfnew = pd.DataFrame({
@@ -42,7 +118,7 @@ def getcp(dfgrouped,logarr):
tmax = tt.max()
if tmax > 500000:
newlen = int(tmax/5000.)
newlen = int(tmax/2000.)
else:
newlen = len(tt)
if newlen < len(tt):

26
rowers/interactiveplots.py
View File

@@ -55,6 +55,7 @@ import rowers.dataprep as dataprep
from rowers.metrics import axes,axlabels,yaxminima,yaxmaxima
from utils import lbstoN
import datautils
watermarkurl = "/static/img/logo7.png"
watermarksource = ColumnDataSource(dict(
@@ -618,28 +619,10 @@ def interactive_otwcpchart(powerdf,promember=0):
# there is no Paul's law for OTW
# Fit the data to thee parameter CP model
fitfunc = lambda pars,x: abs(pars[0])/(1+(x/abs(pars[2]))) + abs(pars[1])/(1+(x/abs(pars[3])))
errfunc = lambda pars,x,y: fitfunc(pars,x)-y
p0 = [500,350,10,8000]
p1 = p0
thesecs = powerdf['Delta']
theavpower = powerdf['CP']
if len(thesecs)>=4:
p1, success = optimize.leastsq(errfunc, p0[:], args = (thesecs,theavpower))
else:
factor = fitfunc(p0,thesecs.mean())/theavpower.mean()
p1 = [p0[0]/factor,p0[1]/factor,p0[2],p0[3]]
p1 = [abs(p) for p in p1]
fitt = pd.Series(10**(4*np.arange(100)/100.))
fitpower = fitfunc(p1,fitt)
p1,fitt,fitpower,ratio = datautils.cpfit(powerdf)
message = ""
#if len(fitpower[fitpower<0]) > 0:
@@ -652,6 +635,7 @@ def interactive_otwcpchart(powerdf,promember=0):
sourcecomplex = ColumnDataSource(
data = dict(
CP = fitpower,
CPmax = ratio*fitpower,
duration = fitt,
ftime = ftime
)
@@ -690,6 +674,7 @@ def interactive_otwcpchart(powerdf,promember=0):
hover.tooltips = OrderedDict([
('Duration ','@ftime'),
('Power (W)','@CP{int}'),
('Power (W) upper','@CPmax{int}'),
])
hover.mode = 'mouse'
@@ -697,6 +682,9 @@ def interactive_otwcpchart(powerdf,promember=0):
plot.line('duration','CP',source=sourcecomplex,legend="CP Model",
color='green')
plot.line('duration','CPmax',source=sourcecomplex,legend="CP Model",
color='red')
script, div = components(plot)
return [script,div,p1,message]

2
rowers/models.py
View File

@@ -232,7 +232,7 @@ class Rower(models.Model):
('hidden','Hidden'),
)
getemailnotifications = models.BooleanField(default=True,
getemailnotifications = models.BooleanField(default=False,
verbose_name='Receive email notifications')
rowerplan = models.CharField(default='basic',max_length=30,

12
rowers/views.py
View File

@@ -2931,15 +2931,7 @@ def otwrankings_view(request,theuser=0,
maxt = 1000.
maxlog10 = np.log10(maxt)
logarr = np.arange(50)*maxlog10/50.
logarr = [int(10.**(la)) for la in logarr]
logarr = pd.Series(logarr)
logarr.drop_duplicates(keep='first',inplace=True)
logarr = logarr.values
logarr = datautils.getlogarr(maxt)
dfgrouped = df.groupby(['workoutid'])
delta,cpvalue,avgpower = datautils.getcp(dfgrouped,logarr)
@@ -8101,8 +8093,10 @@ def team_view(request,id=0):
if inviteid:
teams.send_invite_email(inviteid)
successmessage = text
messages.info(request,successmessage)
else:
message = text
messages.error(request,message)
elif request.user == t.manager:
inviteform = TeamInviteForm()